We study the classification efficacy, neuron responsiveness, and network sparsity of MCDropout and certainty model on three benchmark classification datasets: MNIST, CIFAR10, and Animal-10n [1]}. We use two different architectures: LeNet5 [2]} and ConvNet, a convolutional neural network architecture with 4 convolutional layers followed by 3 fully connected layers. To maximize the effect of MCDropout, we use an all-layer MCDropout architecture where each layer in the certainty model is augmented with MCDropout. Our investigation compares the findings for the original certainty model and its augmented all-layer MCDropout model.
